Scattered photons are produced in a subject being x-ray imaged either from x-ray Compton interactions (which is the dominant form) and x-ray Rayleigh interactions (the less dominant form). These scattered photons degrade CT and CBCT image quality by contributing a background fluence to image pixels that decreases the signal-to-noise-ratio (SNR). It is desirable to identify the scatter contributions in an image and thereby make it possible to remove the scatter from the acquired image to greatly improve image quality. X-ray scatter due to the Compton interaction is difficult to measure directly without disturbing the image itself. Instead of direct measurement of x-ray scatter a method can be used to calculate the scatter. First order Compton scatter interactions have been modeled by the Klein-Nishina (KN) formula. Direct implementation to calculate Compton scattered photons using the KN point model can be accomplished by dividing a volumetric object into (N) (N) (N) voxels. Thus, when performing a calculation using the KN model, the calculation expense for using the point model is N3×M2 calculations (wherein M is a dimension in two dimensional space of the three dimensional object projected/reduced into two dimensions). It would thus be desirable to be able to reduce the number of calculations needed to identify scatter contributions, and to do so faithfully without greatly diminishing the image quality, and in fact to enable the improvement in image quality by removal of calculated scatter from the image.
Cone Beam Computed Tomography (CBCT) is an imaging technology that has been used in several fields of medicine such as in cardiac imaging, radiotherapy, and in dentistry.
Cone-beam computed tomography or CBCT scanning makes it possible to improve image capture and processing speeds by directing, in contrast to fan-beam computed tomography (conventional CT), a cone-beam source toward an object/subject and obtaining a series of projection images on a flat-panel X-ray detector. In cone-beam computed tomography scanning, a 3-D image is reconstructed from numerous individual scan projections, each taken at a different angle about the subject, whose image data is aligned and processed in order to generate and present data as a collection of volume pixels or voxels.
The processing of CBCT data for obtaining images requires some type of reconstruction algorithm. Various types of image reconstruction have been proposed, generally classified as (i) exact or approximate, or (ii) iterative or analytic. Exact cone-beam reconstruction algorithms, based on theoretical work of a number of researchers, require that the following sufficient condition be satisfied: “on every plane that intersects the imaged object there exists at least one cone-beam source”. The widely used Grangeat algorithm, familiar to those skilled in CBCT image processing, is limited to circular scanning trajectory and spherical objects. Only recently, with generalization of the Grangeat formula, is exact reconstruction possible in spiral/helical trajectory with longitudinally truncated data.
In medical applications, CBCT may be used, such as for cardiac imaging, in multiplanar soft tissue imaging, enhanced pretreatment target lesion road-mapping and guidance, and the ability for immediate multi-planar post-treatment assessment. Use of CBCT may translate to a reduction in the use of iodinated contrast media, a decrease in the radiation dose to a patient and an operator, and an increase in the therapeutic index of the patient. In external beam radiation treatment, CBCT is a main imaging modality used for image-guided radiation treatment (IGRT). The CBCT is performed immediately before the radiation treatment to confirm and validate the patient and radiotherapy target position.
CBCT results in a cone-shaped x-ray bundle, with the x-ray source and planar detector (Image Intensifier or in modern days a digital, electronic Flat Panel Detector) rotating around a point (or field) of interest of an object (or alternatively, a patient). The conical shape of the beam distinguishes this technique from helical, conventional CT, which used a fan-shaped beam. As a result of the acquisition of two-dimensional projections throughout this rotation, only one rotation or less is needed to acquire a full three-dimensional dataset. The images received by the detector are then compiled by a computer into volumetric data (primary reconstruction). The image can be visualized as two-dimensional multi-planar reformatted slices or in three dimensions by using surface reconstruction or volume rendering.
The use of CBCT has steadily increased, and the market for CBCT systems has been growing. However, there are some drawbacks that result from data collection methodologies. One drawback is that high-level scattered x-ray radiation generated by the irradiated volume is also received by the planar x-ray image receptor that significantly impairs image quality by creating image reconstruction artifacts and substantially increasing noise. Currently available methodologies to address these image quality problems tend to be expensive and time consuming (in computational time due to the very large 3D data sets). Accordingly, there is a need in the art to address the image quality problems associated with CBCT due to the large amount of x-ray scatter formed by use of a cone beam of x rays.